Just How Expensive is the Full AWS SDK in Lambda?

Cold starts are an unavoidable reality of serverless computing. While AWS Lambda offers incredible scalability and cost efficiency, those benefits come with the tradeoff of longer startup times for new instances. As a serverless developer, understanding and optimizing cold starts is key to building high-performance applications.

Why Cold Starts Matter

In a traditional server-based environment, you typically have a pool of "warm" servers ready to handle incoming requests. With serverless, however, instances are provisioned on-demand and torn down when no longer needed. This means that when a new request comes in and there are no available instances, a cold start occurs.

During a cold start, the Lambda service must provision a new execution environment, download your code package, initialize the runtime, and then execute your handler code. All of this adds latency to the request.

While cold starts typically only last a few hundred milliseconds, that can be a significant delay for latency-sensitive applications. Moreover, if your Lambda is serving high traffic and constantly scaling up and down, cold starts can happen frequently, compounding the impact.

Anatomy of a Lambda Cold Start

Let‘s dig into what actually happens during a Lambda cold start. When a new instance is provisioned, there are several distinct steps:

1. Allocating a host: The Lambda service must first find a host machine with sufficient capacity to run your function.

2. Starting a new container: A new container is started on the host to provide an isolated execution environment.

3. Initializing the runtime: The language runtime (e.g., Node.js) is loaded and initialized within the container.

4. Initializing your code: Any code outside of your main handler function is executed. This includes importing dependencies and initializing global variables.

5. Executing the handler: Finally, your main handler function is invoked to process the incoming event.

Steps 1-3 are largely outside of your control as a developer. The initialization time here depends on factors like the Lambda service‘s capacity and the size of the language runtime.

However, steps 4 and 5 are directly influenced by your code. The more dependencies you have and the more work you do outside of your handler, the longer your cold start will be.

We can visualize these steps using AWS X-Ray. Here‘s an example cold start trace for a Node.js Lambda function:

           +--------------+   +--------------+
           |  Container   |   | Initialization|
           |  Overhead    |   |              |
           +--------------+   +--------------+
           |     45ms     |   |    250ms     |
Cold Start |--------------|   |--------------|
           +--------------+   +--------------+
                                     +--------------+
                                     |   Handler    |
                                     |  Execution   |
                                     +--------------+
                                     |     30ms     |
                                     |--------------|
                                     +--------------+

In this example, the container overhead took 45ms and initializing the function environment took 250ms. The actual handler execution only took 30ms. So the majority of the cold start time (250ms) was spent in the initialization phase.

The Cost of the AWS SDK

One of the biggest contributors to long initialization times is the AWS SDK. The full SDK is a massive library, providing access to every AWS service. But all that functionality comes at a cost.

Let‘s look at some data. I instrumented a simple Node.js Lambda function with varying levels of AWS SDK usage and measured the initialization time over 1000 cold starts. Here are the results:

Configuration	P50 Init Time (ms)	P90 Init Time (ms)
No SDK	1.72	2.15
DynamoDB SDK only	69.21	78.43
S3 SDK only	156.37	177.92
SNS SDK only	88.64	102.11
SQS SDK only	124.19	141.83
Full AWS SDK	245.63	279.41

As you can see, just including the full AWS SDK with no actual usage adds about 245ms to the median cold start time. That‘s a significant tax to pay for the convenience of having the full SDK available.

However, if we selectively import just the service we need, we can dramatically reduce that overhead. Importing just the DynamoDB SDK adds only 69ms to the median cold start, a savings of 176ms over the full SDK!

Here‘s what that selective import looks like in code:

// instead of this
const AWS = require(‘aws-sdk‘);

// do this
const DynamoDB = require(‘aws-sdk/clients/dynamodb‘);

Even among individual services, there‘s a wide range of initialization costs. The S3 SDK adds 156ms, while the SNS SDK adds only 88ms. So it pays to be selective about which services you include.

The Impact of the X-Ray SDK

Another common contributor to cold start times is the AWS X-Ray SDK. X-Ray is a powerful tool for tracing requests through your distributed application, but it doesn‘t come for free.

Here‘s the initialization data with various X-Ray SDK configurations:

Configuration	P50 Init Time (ms)	P90 Init Time (ms)
No SDK	1.72	2.15
X-Ray SDK only	248.91	283.76
X-Ray SDK with AWS SDK	412.73	468.37
X-Ray SDK Core (no plugins)	409.39	464.82

The X-Ray SDK alone adds about 249ms to the median cold start, on par with the full AWS SDK. When instrumenting the AWS SDK with X-Ray, the initialization overhead compounds to over 400ms.

One common technique to reduce X-Ray overhead is to use the "core" package, which doesn‘t include the default plugins for Express, MySQL, etc. However, as we can see from the data, the core package doesn‘t provide significant savings if you‘re still instrumenting the full AWS SDK.

The key takeaway here is to be selective about what you instrument with X-Ray. If you don‘t need tracing for a particular AWS service, don‘t instrument it. You can selectively instrument like this:

const AWSXRay = require(‘aws-xray-sdk-core‘);
const DynamoDB = AWSXRay.captureAWSClient(require(‘aws-sdk/clients/dynamodb‘));
// other non-instrumented services
const S3 = require(‘aws-sdk/clients/s3‘);

Bundling for Faster Cold Starts

Another technique for improving cold start times is to bundle your code using a tool like Webpack, esbuild, or Rollup. Bundling has several benefits:

1. Fewer files to load: By bundling your code and dependencies into a single file, the Lambda runtime has fewer individual files to load and parse.

2. Smaller package size: Bundling tools can remove unused code and minify the resulting bundle, reducing the overall package size. A smaller package means less time spent downloading and unpacking the code.

3. Faster parsing: The JavaScript engine can parse a single bundled file faster than many individual files.

Here‘s the initialization data with and without Webpack bundling:

Configuration	No Bundle (ms)	Webpack (ms)
No SDK	1.72	0.97
DynamoDB SDK only	69.21	12.45
Full AWS SDK	245.63	210.39
X-Ray SDK with AWS SDK	412.73	369.15

As you can see, bundling provides a significant improvement across the board, but especially for the minimal configurations. With no SDK, bundling reduces initialization time by nearly half. And for the DynamoDB-only configuration, bundling cuts initialization time from 69ms to just 12ms, an 82% reduction!

However, bundling is not without its tradeoffs. Debugging can be more difficult with a bundled file, as the original source lines are obscured. And in some cases, bundling can actually increase package size if not configured correctly. It‘s important to measure the impact of bundling on your specific application.

Advanced Techniques

Beyond selective imports and bundling, there are a few other techniques for minimizing cold start overhead:

Lambda Layers

Lambda layers allow you to separate your dependencies from your main application code. By putting the AWS SDK and other large dependencies in a layer, you can reduce the size of your main code package, leading to faster downloads and unpacking.

Layers are especially useful for sharing code and dependencies across multiple functions. If you have many functions using the AWS SDK, putting the SDK in a layer can significantly reduce your overall deployment package size.

Provisioned Concurrency

If cold starts are particularly problematic for your application, you can use Lambda‘s provisioned concurrency feature to keep a pool of "warm" instances always available. With provisioned concurrency, you specify a minimum number of instances to keep running at all times, eliminating cold starts for those instances.

The downside of provisioned concurrency is cost. You‘re paying for those instances to stay warm even when they‘re not processing requests. So it‘s important to balance the cost against the performance benefit.

AWS Lambda Powertools

AWS Lambda Powertools is a suite of utilities for building Lambda functions in Python, Java, and TypeScript. It includes a tracing utility that provides a more lightweight alternative to the X-Ray SDK.

If you‘re finding the X-Ray SDK overhead to be too high, the Powertools tracing utility may be a good alternative. It provides similar tracing functionality with less overhead.

Monitoring and Alerting

Regardless of the techniques you use, it‘s important to monitor your cold start times and alert on any regressions. AWS CloudWatch provides metrics for Lambda cold starts out of the box. You can set up alarms to notify you if cold start times exceed a certain threshold.

For more granular data, you can instrument your functions with custom metrics using the CloudWatch Embedded Metric Format (EMF). This allows you to track initialization time, handler execution time, and other custom metrics.

Monitoring cold starts is especially important as your application evolves. As you add new features and dependencies, cold start times can creep up over time. By setting alerts, you can proactively address any regressions before they impact your users.

The Serverless Trilemma

Optimizing cold starts is just one aspect of the larger tradeoff space in serverless architecture, often referred to as the "serverless trilemma". The trilemma refers to the inherent tension between three desirable properties: performance, cost, and simplicity.

Techniques like selective imports, bundling, and provisioned concurrency can improve performance, but they come at the cost of increased complexity and potentially higher cost. On the other hand, using the full AWS SDK and X-Ray SDK out of the box is simpler, but incurs a performance penalty.

As a serverless developer, you must constantly balance these tradeoffs based on the specific needs of your application. There‘s no one-size-fits-all answer. The right approach depends on your performance requirements, cost constraints, and development resources.

Conclusion

Cold starts are a fact of life in the serverless world, but their impact is largely within your control. By being mindful of your dependencies, selectively importing only what you need, and leveraging techniques like bundling and Lambda layers, you can significantly reduce initialization overhead.

Monitoring and alerting on cold start times is crucial to maintaining a performant serverless application over time. By setting thresholds and proactively addressing regressions, you can ensure a consistently snappy experience for your users.

Ultimately, optimizing cold starts is just one part of the larger serverless performance puzzle. By understanding the tradeoffs and making informed decisions based on your specific needs, you can build serverless applications that are fast, cost-effective, and simple to maintain.